Polygenic Risk Score Simulation: A case study of sudden cardiovascular diseases derived from literature insights

Sudden cardiovascular diseases, including sudden cardiac arrest (SCA) and myocardial infarction (MI), remain among the leading causes of mortality worldwide. This study employs synthetic data simulations to develop polygenic risk scores (PRS) for improved identification of individuals at elevated risk. Leveraging genetic and phenotypic information from established literature, we modeled associations between multiple single nucleotide polymorphisms (SNPs) and the risk of sudden cardiovascular diseases. Our case study demonstrated that individuals with higher PRS values have a significantly increased risk of SCA and MI, with SNPs such as LTA (252A>G) identified as notable contributors. Additionally, combining PRS with traditional cardiovascular risk factors, such as smoking and diabetes, improved predictive accuracy, underscoring the value of integrating genetic data with clinical variables. These findings highlight the cumulative effect of genetic predispositions in determining cardiovascular risk and suggest potential applications of PRS models in personalized medicine to enhance preventive strategiesWhile promising, the study recognizes limitations related to the use of synthetic data and the need for validation in diverse, real-world populations. Future research should focus on refining these models and exploring additional genetic markers to further improve prediction capabilities.